A simulation study comparing supertree and combined analysis methods using SMIDGen

In this paper, we describe an extensive simulation study we performed comparing two supertree methods, MRP and weighted MRP, to combined analysis methods on large model trees. A key contribution of this study is our novel simulation methodology (Super-Method Input Data Generator, or SMIDGen) that better reflects biological processes and the practices of systematists than earlier simulations. We show that combined analysis based upon maximum likelihood outperforms MRP and weighted MRP, giving especially big improvements when the largest subtree does not contain most of the taxa.This study demonstrates that MRP and weighted MRP produce distinctly less accurate trees than combined analyses for a given base method (maximum parsimony or maximum likelihood). Since there are situations in which combined analyses are not feasible, there is a clear need for better supertree methods. The source tree and combined datasets used in this study can be used to test other supertree and combined analysis methods.Supertree methods-methods that, given a set of trees with overlapping sets of taxa, return a tree on the combined taxon set-offer one approach to estimating phylogenies from multi-marker datasets. Supertree estimation methods are of considerable interest in the systematics community, and several large phylogenies have been published using these methods [1].Matrix representation with parsimony (MRP) [2,3] is currently the most widely used supertree method. It operates by encoding the set of source trees as a matrix of partial binary characters, one character for each branch of each source tree, and then analyzing that matrix using a parsimony heuristic. Weighted MRP [4] is a variant of MRP in which the partial binary characters are weighted, and this weighted matrix representation is then analyzed using weighted parsimony. These character weights are obtained from the source tree analyses, using either bootstrap support or posterior probabilities to assign weights to the branc